code: def left_right_halo_exchange(
      self,
      left_output_halo,
      right_output_halo,
      left_input_halo=None,
      right_input_halo=None,
  ):
      inplace = (
          False
          if left_input_halo is None and right_input_halo is None
          else True
      )
      if not inplace:
          left_input_halo = torch.empty_like(right_output_halo)
          right_input_halo = torch.empty_like(left_output_halo)
      channels_last = (
          left_output_halo.is_contiguous(
              memory_format=torch.channels_last
          )
          and not self.explicit_nhwc
      )
      left_tx = self._allocate_peer_tensor(left_input_halo)
      right_tx = self._allocate_peer_tensor(right_input_halo)
      pm.push_pull_halos_1d(
          self.diagnostics,
          self.explicit_nhwc,
          self.numSM,
          self.rank_in_group,
          self.left_zero,
          left_output_halo,
          left_tx[self.rank_in_group],
          right_tx[self.wrap_around_left_rank_in_group],
          left_input_halo,
          self.right_zero,
          right_output_halo,
          right_tx[self.rank_in_group],
          left_tx[self.wrap_around_right_rank_in_group],
          right_input_halo,
      )
      if not inplace:
          return left_input_halo, right_input_halo
  
imports: import torch
  import torch.distributed
  from torch import nn
  import nccl_p2p_cuda
  import peer_memory_cuda
label_desc: Use image node_id 3 for calling the HaloExchangerPeer obj's underlying member method code with example usage: obj.left_right_halo_exchange(left_output_halo, right_output_halo, left_input_halo, right_input_halo) and returns: left_input_halo,  right_input_halo